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Month 2 Plan
canquesse edited this page Jul 29, 2026
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This month you add the "intelligence" layer to the project. Careful: this month is where most people stop. The real difference shows up in Month 3, when you measure the quality of the work.
- LLM fundamentals (conceptual): what an LLM is, token, context window, temperature
- Neural networks and transformers (conceptual — without the math)
- Tool/function calling
- Agent loop: think → select a tool → call → observe → think again
- RAG as one of the agent's tools: embedding, chunking, similarity search
- Token and context window
- Embedding vector
- Transformer and attention (conceptual)
- Tool calling contract
- Agent loop (reasoning loop)
- System prompt
- State management
- Infinite loop risk and max steps
- LLM client (API key from an environment variable, never hardcoded)
- Tool #1: RAG search (chunking + embedding + pgvector)
- Tool #2: a deterministic simple tool (calculation / date)
- Agent loop — each turn produces an
agent_steprecord -
max_stepssafety limit - The "start task" endpoint in control-plane is opened to the end user
- Two different system prompts are tried, and the behavioral difference is observed
Why at least two tools? With a single tool the agent doesn't decide, it just calls that tool — that's a plain function call. With two tools the question "which one, when" arises; that's the behavior that makes an agent an agent.
Split the tasks between you and swap at the mid-month rotation — you decide who takes what. Two rough tracks: (a) agent loop + LLM client, (b) RAG tool + pgvector; then swap so both of you touch both.
See roadmap.md
- "Agent thinking" screen recording — the inside of an AI agent's head
- "What is tool calling, in 60 seconds"
- A "failure moment" clip showing the step limit kicking in after pushing it into an infinite loop
- Month 1 ↔ Month 2 comparative progress video
AgentLens
Project
Working Cadence
Months
Reference